Considering the problems existed in the saliency region detected out by the current familiar saliency detection methods: its boundary is sparse and unclear, and its inside is uneven and non-compact, a method called saliency detection based on the conditional random field and image segmentation is proposed. This method comprehensively utilizes boundary information, local information and global information to extract a variety of salient features from an image. By fusing these features in the framework of conditional random field, a coarse detection for saliency region is realized based on region labeled of saliency region and background region, and then a fine detection for saliency region is realized through combining the result of region labeled with an interactive image segmentation method. Experimental results show that the proposed approach can clearly and accurately extract saliency regions and improve the detection precision.